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Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model
Soil and Tillage Research ( IF 6.5 ) Pub Date : 2020-10-21 , DOI: 10.1016/j.still.2020.104838
Ruiyuan Li , Miaoqing Xu , Ziyue Chen , Bingbo Gao , Jun Cai , Feixue Shen , Xianglin He , Yan Zhuang , Danlu Chen

The spatial and temporal variations of crop species and rotation types play a key role in crop yield estimation, natural resources management and climate change research. Remote sensing is an effective approach for monitoring agricultural management at the regional scale. However, the lack of remote sensing data with high spatiotemporal resolutions results in a generally limited crop mapping accuracy. To overcome this limitation, we employed an improved flexible spatiotemporal data fusion (IFSDAF) model to conduct data fusion using MODIS and Landsat imagery and extract NDVI time series with both high spatial and temporal resolution. Following this, we proposed a Random-Forest-based model and a decision-rule-based model for mapping crop species and rotation types. According to the accuracy assessment, the Random-Forest-based model with an overall accuracy of 90 % was more suitable for mapping crops with two or more growing seasons, such as double-cropping rice. Meanwhile, the decision-rule-based model with an overall accuracy of 89.7 % was more suitable for monitoring crops with only one growing season compared to the Random-Forest-based model. In comparison with traditional ways such as field survey, the classification models proposed in this study provide useful information for better monitoring spatiotemporal variations of crops species and rotation types, and guiding agricultural management accordingly.



中文翻译:

使用融合的MODIS和Landsat数据对作物种类和轮作类型进行基于物候分类:基于随机森林模型和基于决策规则模型的比较

作物种类和轮作类型的时空变化在作物产量估算,自然资源管理和气候变化研究中发挥着关键作用。遥感是监测区域规模农业管理的有效方法。但是,缺少具有高时空分辨率的遥感数据导致作物测绘精度普遍受到限制。为了克服此限制,我们采用了改进的灵活时空数据融合(IFSDAF)模型,以使用MODIS和Landsat影像进行数据融合,并提取具有高时空分辨率的NDVI时间序列。在此之后,我们提出了一个基于随机森林的模型和一个基于决策规则的模型来绘制作物种类和轮作类型。根据准确性评估,基于随机森林的模型(总体精度为90%)更适合于绘制两个或两个以上生长季节的农作物,例如双季稻。同时,与基于随机森林的模型相比,基于决策规则的模型的整体准确性为89.7%,它更适合于仅监测一个生长季节的作物。与田间调查等传统方式相比,本研究提出的分类模型为更好地监测农作物种类和轮作类型的时空变化提供了有用的信息,并据此指导农业管理。与基于Random-Forest的模型相比,只有7%的生长季节更适合于监测作物。与田间调查等传统方式相比,本研究提出的分类模型为更好地监测农作物种类和轮作类型的时空变化提供了有用的信息,并据此指导农业管理。与基于Random-Forest的模型相比,只有7%的生长季节更适合于监测作物。与田间调查等传统方式相比,本研究提出的分类模型为更好地监测农作物种类和轮作类型的时空变化提供了有用的信息,并据此指导农业管理。

更新日期:2020-10-30
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